FallUP – Fall Risk Assessment and Prediction Based on Gait Analysis

Project Title

FallUP – Fall Risk Assessment and Prediction Based on Gait Analysis

Project Area of Specialization Internet of ThingsProject Summary

Fall represents a serious health threat to the citizens with elderly having 65 years of age or above, or with a disability. Observed falls are of many kinds: falls from walking or standing, falls from standing on supports, e.g. ladders etc. falls from sleeping or lying in the bed and falls from sitting on a chair. That these kinds of falls are very common in lives of almost all senior citizens and disable persons. This project deals with the risk assessment and prediction of a fall based on Gait Analysis.

Fall represents a serious health threat to the citizens with elderly having 65 years of age or above, or with a disability. Observed falls are of many kinds: falls from walking or standing, falls from standing on supports, e.g. ladders etc. falls from sleeping or lying in the bed and falls from sitting on a chair. That these kinds of falls are very common in lives of almost all senior citizens and disable persons. This project deals with the risk assessment and prediction of a fall based on Gait Analysis.Project Objectives

To help keeping up with the health of Elderly people having low strength rate than healthier, younger people. To help Health-care sectors in better understanding the falls and towards the fall behaviours. To alert the Caregivers of the sick or elderly people in quickly providing medical assessments to those under observation. To provide help in lowering health care expenditure, raised due to fall injuries.

• To help keeping up with the health of Elderly people having low strength rate than healthier, younger people.
• To help Health-care sectors in better understanding the falls and towards the fall behaviours.
• To alert the Caregivers of the sick or elderly people in quickly providing medical assessments to those under observation.
• To provide help in lowering health care expenditure, raised due to fall injuries.

Project Implementation Method

Data is collected in the form of different parameters of gait. I-e Gait and converted into a single gait value using numerical computations. On the basis of information collected threshold is determined of correct walk pattern. Test objects are examined using our product .Anomalies are detected if any in the walk pattern of the old age and disabled people

Data is collected in the form of different parameters of gait. I-e Gait and converted into a single gait value using numerical computations. On the basis of information collected threshold is determined of correct walk pattern. Test objects are examined using our product .Anomalies are detected if any in the walk pattern of the old age and disabled peopleBenefits of the Project

Our project aims at benefiting the following areas/people:

• Elderly people having low strength rate than healthier, younger people; who are affected by falls the most.
• Health-care sectors in better understanding the falls and towards the fall behaviours.
• Caregivers of the sick or elderly people in quickly providing medical assessments to those under observation.
• To provide help in lowering health care expenditure, raised due to fall injuries.

Technical Details of Final Deliverable

To determine the gait cycle of an individual, some phases of gait must be visualized, these are Initial Contact, Loading Response, Mid Stance, Terminal Stance, and these four phases identify a normal gait cycle. Furthermore, in our project we have to propose a fall prevention mechanism i.e. detect the fall and detect towards the fall behavior. Phases to identify this behavior are Pre-Swing, Initial Swing, Mid Swing, and Terminal Swing. All the values collected through this phase study via the use of wearable sensors or Kinect would be used to determine the threshold for gait analysis data. All this data would be saved on some Android device where we have a trained model for determining anomaly or towards anomaly behavior and prompt with the feedback according to the received information

To determine the gait cycle of an individual, some phases of gait must be visualized, these are Initial Contact,
Loading Response, Mid Stance, Terminal Stance, and these four phases identify a normal gait cycle. Furthermore,
in our project we have to propose a fall prevention mechanism i.e. detect the fall and detect towards the fall
behavior. Phases to identify this behavior are Pre-Swing, Initial Swing, Mid Swing, and Terminal Swing. All the
values collected through this phase study via the use of wearable sensors or Kinect would be used to determine
the threshold for gait analysis data. All this data would be saved on some Android device where we have a trained
model for determining anomaly or towards anomaly behavior and prompt with the feedback according to the
received informationFinal Deliverable of the Project HW/SW integrated systemType of Industry IT , Medical , Health Technologies Artificial Intelligence(AI), Internet of Things (IoT), Big DataSustainable Development Goals Good Health and Well-Being for People, Life on LandRequired Resources

To help keeping up with the health of Elderly people having low strength rate than healthier, younger people. To help Health-care sectors in better understanding the falls and towards the fall behaviours. To alert the Caregivers of the sick or elderly people in quickly providing medical assessments to those under observation. To provide help in lowering health care expenditure, raised due to fall injuries.